Methods and systems for smart claim routing and smart claim assignment

ABSTRACT

A method of assigning and/or routing an auto claim to an appropriate claim handling tier to mitigate delay may include training a machine learning model using historical claim data to determine a severity corresponding to an injury claim, receiving a loss report corresponding to an auto accident, analyzing the loss report using the trained machine learning model to determine the severity of at least one injury corresponding to the loss report, determining an injury segment, and storing the indication of the injury segment in association with the loss report.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority to U.S. Application No.62/671,253, filed May 14, 2018. The priority application, U.S.62/671,253 is hereby incorporated by reference.

FIELD OF INVENTION

This disclosure generally relates to smart claim routing and smart claimassignment. More specifically, the present disclosure is directed totraining a machine learning model using historical claim data todetermine a severity of an injury and a routing and/or assignment of theclaim based on the severity.

BACKGROUND

In various applications a need exists to quickly and accurately assignand/or route vehicle insurance and/or injury claims to one or more claimhandlers who are situated in one or more tiers. Traditionally, autoand/or injury claims are assigned to a default, or “catch all”, pool ofclaims, wherein the claims may remain idle until such time that a claimhandler reviews the claim and assigns it to an appropriate claimhandling tier. Efforts to organize claims into proper filing categorieshave been attempted, but rely on customer input and self-categorization.Customers may be incentivized to assign higher-than-warranted severityto claims in order to result in faster processing times. Furthermore, nohumans, regardless of skill level, are able to analyze the entirety ofall claims filed historically in an insurer's course of business tofacilitate assignment and/or routing of claims. No humans, regardless ofskill level, are able to analyze all ancillary documents filed withclaims (e.g., electronic medical records) in a tractable period of time.

Claim handlers are lacking in experience and may improperly assign orimproperly route a loss report. The varied experience of claim handlers,and limited tooling, are problems in the prior art. For example, apolicyholder may be involved in an accident, and may be injured. Thepolicyholder may inform the insurer that the injury occurred, andprovide the insurer with written and/or verbal documentation relating tothe incident (e.g., the vehicle make and model, year, mileage, hospitalname, general nature of injury, length of hospital stay, etc.). Next, anauto claim handler may manually review the provided information, andmake a determination of severity based on the claim handler'sexperience. However, conventional systems are unable to sufficientlyidentify/formulate precise characterizations of loss without resort tounconscious biases, and are unable to properly weight all historicaldata in determining loss mitigation factors in order to produceassignments and routes of loss reports that may be quantified, repeated,and whose accuracy can thus be improved.

Furthermore, claim handlers lack the technical ability to analyze allpast claims in a very short time (e.g., in microseconds). Claimhandlers' lack of experience, cognitive bias, fatigue, etc. may leadthem to make errors in judgment as to whether an injury is severe ornot. Important facts, such as the presence of electronic medical recordsincluding unfamiliar jargon (e.g., “ischemia”) may not be understood byclaim handlers who lack a background in medicine. Arcane legalpleadings, such as complaints wherein the party identified in the lossreport as the injured party is listed as a complainant or plaintiff, maynot be understood by claim handlers. As such, a need exists forcomputerized methods and systems of automatically assigning and/orrouting claims related to vehicle insurance and personal injury, whereinthe methods and systems can be continuously trained on new data, operatearound the clock, and predict results that are repeatable andquantifiable.

BRIEF SUMMARY

The present disclosure generally relates to systems and methods forsmart claim routing and smart claim assignment. Embodiments of exemplarysystems and computer-implemented methods are summarized below. Themethods and systems summarized below may include additional, fewer, oralternate components, functionality, and/or actions, including thosediscussed elsewhere herein.

In one aspect, the present embodiments may relate to assigning and/orrouting an auto claim to an appropriate claim handling tier to mitigatedelay, the method including training a machine learning model usinghistorical claim data to determine a severity of an injury, receiving aloss report corresponding to an auto accident, analyzing the loss reportusing the trained machine learning model to determine the severity of aninjury, determining an injury segment based on the severity of theinjury claim, and storing an indication of the injury segment inassociation with the loss report.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each figuredepicts one embodiment of a particular aspect of the disclosed systemand methods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 depicts an environment for analyzing the severity of an injuryclaim and assigning and/or routing the claim to a set of tiers,according to an embodiment,

FIG. 2 depicts an environment for training a machine learning model toanalyze the severity of an injury claim and assign and/or route theclaim, as depicted in FIG. 1, according to one embodiment;

FIG. 3 depicts an example environment for analyzing the severity of aninjury claim and assigning and/or routing the claim to a set of tiers asdepicted in FIG. 1, and/or training a machine learning model as depictedin FIG. 2, according to one embodiment and scenario,

FIG. 4 depicts content of an exemplary electronic claim record that maybe processed by a machine learning model, in one embodiment; and

FIG. 5 depicts a flow diagram of an exemplary computer-implementedmethod for assigning and/or routing an auto claim to an appropriateclaim handling tier to mitigate delay, according to one embodiment.

The figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

The present embodiments are directed to, inter alia, machine learningtechniques for training a machine learning (ML) model using historicalautomobile claim data to determine a routing and/or assignment for anauto insurance claim. Systems and methods may include natural languageprocessing of free-form notes/text, or free-form speech/audio, recordedby call center and/or claim adjustor, photos, and/or other evidence. Thefree-form text and/or free-form speech may be received from a customerwho is inputting the text and/or speech into a mobile deviceapplication, into a telephone system, and/or into a chat bot orrobo-advisor.

Other inputs to a machine learning/training model may be harvested fromhistorical claims, and may include make, model, year, miles,technological features, vehicle telematics, and/or other characteristicsof a vehicle, whether a claim is paid or not paid, descriptions and/oraccounts of injuries to vehicle operators, passengers, and/or thirdparties, liability (e.g., types of injuries, where treated, how treated,etc.), disbursements related to claim such as payment of medical bills,hotel costs and other payouts, etc. The present embodiments maydynamically characterize insurance claims, and/or dynamically classify,categorize, and/or measure the severity of an injury to an automobileoperator, passenger, and/or third party. In general, herein, an“operator” may refer to a vehicle operator/driver, passenger,pedestrian, or any person within range of an automobile.

As noted above, the present embodiments may also be directed to machinelearning and/or training a model using historical auto-related injuryclaim data to determine severity of injury, and routing claimsaccordingly. The present embodiments may include natural languageprocessing of free-form notes recorded by call center and/or claimadjustor (e.g., “hit a deer”, “broken leg”, “collision”, etc.), as wellas photos, and/or other material to use as input to machinelearning/training model. In an embodiment, a preexisting condition of aclaimant may be considered in training the neural network. For example,claims in which the claimant has a heart condition, high blood pressure,diabetes or another condition may be labeled as higher priority claims.Inputs to the ML models may also include information relating topreexisting conditions, including information sourced from electronicmedical records, wherein patients/policyholders have opted-in to theprovision and analysis of such records.

Exemplary Environments for Training and/or Operating a Machine LearningModel to Route a Claim and/or Assign a Claim

The embodiments described herein may relate to, inter alia, training andoperating a machine learning model to route a claim and/or assign aclaim. More particularly, in some embodiments, one or more ML models maybe trained using historical claims data as training input. Anapplication may be provided to a client computing device (e.g., asmartphone, tablet, laptop, desktop computing device, wearable, or othercomputing device) of a user. A user of the application, who may be anemployee of a company employing the methods described herein or acustomer of that company, may enter input into the application via auser interface or other means in the creation of a loss report and/orclaim report. The input may be transmitted from the client computingdevice to a remote computing device (e.g., one or more servers) via acomputer network, and then processed further, including by applyinginput entered into the client to the one or more trained ML models toproduce outputs and weights indicating type and/or severity of injury.The type and/or severity of injury may be identified in electronic claimrecords, and/or may be predictive of certain real-world injuries.Although historical claims may be used in training one or more neuralnetwork models, electronic claims information may be streaming inrealtime or with near-realtime latencies (e.g., on the order of 10 ms orless), in a dynamic process.

For example, an analysis engine may receive the input and determine,using a trained ML model, the input to determine one or more injuryand/or a severity levels of the respective one or more injury. Hereinseverity levels may be expressed numerically, as strings (e.g., aslabels), or in any other suitable format. Severity levels may beexpressed as Boolean values (e.g., severe/not severe), scaled quantities(e.g., from 0.0-1.0), or in any other suitable format. The determinedinjury and/or severity levels may be displayed to the user, and/or maybe provided as input to another application (e.g., to an applicationwhich uses the severity levels to assign a claim to one or more injurysegment/tier or for other purposes).

Turning to FIG. 1, an environment 100 for analyzing the severity of aninjury claim and assigning and/or routing the claim to a set of tiers isdepicted, according to an embodiment. The environment 100 may include aninjury claim 102, a severity analysis component 104, and a tier set 106.The tier set 106 may include a plurality of tiers 108-1 to 108-n,wherein n may be any integer. That is, any number of tiers may bepresent. In addition, in some embodiments, the tier set 106 may be ahierarchical set (e.g., a tree). For example, a first tier maycorrespond to a first set of sub-tiers, a second tier may correspond toa second set of sub-tiers, and so on. The injury claim 102 may be a lossreport, which may be an electronic file defining information relating toa claimant/policy holder (e.g., name, address, telephone number, etc.),accident information (e.g., date, time, and location of loss), a diagramand/or photograph of the accident, a textual description of thecircumstances of the injury, and information relating to any injuredparties/operators. The severity analysis component 104 of environment100 may include instructions for operating a trained machine learning(ML) model. For example, severity analysis component 104 may receive aninjury claim and/or loss report, and may extract a set of discretefields from the loss report. The fields may correspond to inputs of thetrained ML model (e.g., a photograph, description, vehicle type, weatherreport, etc.). The severity analysis component 104 may input the fieldsinto the trained ML model, and may receive an output from the trained MLmodel, in a blocking or non-blocking fashion. That is, the severityanalysis component 104 may execute in a separate thread of anapplication or in a main thread of an application.

The trained ML model may output an indication of severity which mayinclude a flat and/or hierarchical set of tiers and/or sub-tiers, or anumeric representation of severity. For example, the ML model may outputa set of tiers a-z wherein tiers a-z may include any number of tiers,and wherein each tier represents a tier in tier set 106. This set oftiers may be an injury segment corresponding to one or more levels ofcustomer service handling, wherein each level is responsible forhandling progressively more severe claims. For example, a first tier mayhandle claims limited to minor property damage, a second tier may handleclaims including property damage with an estimated repair value lessthan $1000, a third tier including property damage with an estimatedrepair value greater than $100 but less than $5000, and so forth. A tierin a-z may handle claims that include any combination of estimatedrepair values, and personal injury wherein the injured person was ableto be treated on an outpatient basis. Another tier in a-z may handleinjury claims wherein the injured person was treated on an inpatientbasis for one day or less. Another tier may handle an injury wherein theinjured person was treated for a week or less. Another tier may handlean injury in which a dismemberment, paralysis, and/or death occurred,and so on.

The ML model may be trained on, and may operate to assign/route claimsto a set of tiers, based on an attributes of the claim other than theseverity of one or more injuries. For example, an ML model may betrained which, in addition to or alternate to examination of claiminjury severity, analyzes historical claims to determine the likelihoodthat an attorney or other representative will represent an injuredparty. An ML model may be trained on historical claims to determine thelikelihood that an injury claim will result in the institution of alegal or quasi-legal proceeding (e.g., the filing of a lawsuit or otherlitigation, the receipt of demand letter, the convening of anarbitration proceeding, etc.). Separate tiers may exist for handlingclaims including, or likely to include, any of the foregoing legal orquasi-legal proceedings, and the ML model may be trained to output anassignment to one or more of the tiers.

FIG. 2 depicts an environment 200 for training a machine learning modelto analyze the severity of an injury claim and assign and/or route theclaim, as depicted in FIG. 1, according to one embodiment. Theenvironment 200 may include an input data 202 and a claim analysisengine 204. The environment 200 may include a machine learning modelunit 206 and an historical data 208 including one or more claims 210-1through 210-n. Machine learning (ML) model unit 206 may further includea training unit 220, and may environment 200 may include a customer data212. The input data 202 may correspond to the injury claim 102 of FIG.1, and may comprise one or more loss reports, as well as demographicinformation of a customer and/or another operator (e.g., name, age,driver's license, etc.). Photographs and descriptions of an accident maybe included in input data 202. The input data 202 may be embodied in anysuitable data storage device including, without limitation, a flat file,an electronic database (e.g., a structured query language (SQL)database, no-SQL database, etc.). The input data 202 may bestored/transmitted in a memory, including in a random access memory(RAM) or via a message queue as part of a distributed computing system.The claim analysis engine 204 may be embodied in a client computingdevice and/or server computing device, as discussed below. The claimanalysis engine 204 may include instructions for parsing and/orotherwise massaging data received from the input data 202, as well asinstructions for querying the input data 202. The claim analysis engine204 may include instructions for operating a ML model trained by MLmodel unit 206, and for performing various actions based on the outputof such trained ML model(s).

Input data 202 and historical data 108 may each comprise a plurality(e.g., thousands or millions) of electronic documents, or otherinformation. As used herein, the term “data” generally refers toinformation related to a vehicle operator, which exists in theenvironment 200. For example, data may include an electronic documentrepresenting a vehicle (e.g., automobile, truck, boat, motorcycle, etc.)insurance claim, demographic information about the vehicle operatorand/or information related to the type of vehicle or vehicles beingoperated by the vehicle operator, and/or other information. Data may behistorical or current. Although data may be related to an ongoing claimfiled by a vehicle operator, in some embodiments, data may consist ofraw data parameters entered by a human user of the environment 200 orwhich is retrieved/received from another computing system. Data may ormay not relate to the claims filing process, and while some of theexamples described herein refer to auto insurance claims, it should beappreciated that the techniques described herein may be applicable toother types of electronic documents, in other domains. For example, thetechniques herein may be applicable to assigning and/or routing claimsrelated to homeowners insurance, agricultural insurance, health or lifeinsurance, renters insurance, etc. In that case, the scope and contentof the data may differ, in addition to the domain-specific training andoperational requirements applicable to trained ML models. Generally,data may comprise any digital information, from any source, created atany time.

The ML model unit may train ML models for analyzing claims to build oneor more ML models. The ML models may be trained to accept a plurality ofinputs, which may include inputs in the input data 202 and from othersources. For example, in some embodiments, customer data may beretrieved/received from customer data 212, and may be input into trainedML models, as described below. In general, the ML model unit 206 mayoperate ML models and training unit 220 may train models by, inter alia,establishing a network architecture, or topology, and adding layers thatmay be associated with one or more activation functions (e.g., arectified linear unit, softmax, etc.), loss functions and/oroptimization functions. Multiple different types of artificial neuralnetworks may be employed, including without limitation, recurrent neuralnetworks, convolutional neural networks, and deep learning neuralnetworks. Data sets used to train the artificial neural network(s) maybe divided into training, validation, and testing subsets; these subsetsmay be encoded in an N-dimensional tensor, array, matrix, or othersuitable data structures. Training may be performed by iterativelytraining the network using labeled training samples. Training of theartificial neural network may produce byproduct weights, or parameterswhich may be initialized to random values. The weights may be modifiedas the network is iteratively trained, by using one of several gradientdescent algorithms, to reduce loss and to cause the values output by thenetwork to converge to expected, or “learned”, values. In an embodiment,a regression neural network may be selected which lacks an activationfunction, wherein input data may be normalized by mean centering, todetermine loss and quantify the accuracy of outputs. Such normalizationmay use a mean squared error loss function and mean absolute error. Theartificial neural network model may be validated and cross-validatedusing standard techniques such as hold-out, K-fold, etc. In someembodiments, multiple artificial neural networks may be separatelytrained and operated, and/or separately trained and operated inconjunction. In another embodiment, a Bayesian model may be used totrain the ML model.

In an embodiment, the trained ML model may include an artificial neuralnetwork (ANN) having an input layer, one or more hidden layers, and anoutput layer. Each of the layers in the ANN may include an arbitrarynumber of neurons. The plurality of layers may chain neurons togetherlinearly and may pass output from one neuron to the next, or may benetworked together such that the neurons communicate input and output ina non-linear way. In general, it should be understood that manyconfigurations and/or connections of ANNs are possible. In anembodiment, the input layer may correspond to input parameters that arenumerical facts, such as the age and/or number of years of workexperience of a person, or to other types of data such as data from theloss report. The input layer may correspond to a large number of inputparameters (e.g., one million inputs), in some embodiments, and may beanalyzed serially or in parallel. Further, various neurons and/or neuronconnections within the ANN may be initialized with any number of weightsand/or other training parameters. Each of the neurons in the hiddenlayers may analyze one or more of the input parameters from the inputlayer, and/or one or more outputs from a previous one or more of thehidden layers, to generate a decision or other output. The output layermay include one or more outputs, each indicating a prediction. In someembodiments and/or scenarios, the output layer includes only a singleoutput. For example, a neuron may correspond to one of the neurons inthe hidden layers. Each of the inputs to the neuron may be weightedaccording to a set of weights W₁ through W_(i), determined during thetraining process (for example, if the neural network is a recurrentneural network) and then applied to a node that performs an operation α.The operation α may include computing a sum, a difference, a multiple,or a different operation. In some embodiments weights are not determinedfor some inputs. In some embodiments, neurons of weight below athreshold value may be discarded/ignored. The sum of the weightedinputs, r₁, may be input to a function which may represent any suitablefunctional operation on r₁. The output of the function may be providedto a number of neurons of a subsequent layer or as an output of the ANN.

The computer-implemented methods discussed herein may includeadditional, fewer, or alternate actions, including those discussedelsewhere herein. The methods may be implemented via one or more localor remote processors, transceivers, servers, and/or sensors (such asprocessors, transceivers, servers, and/or sensors mounted on drones,vehicles or mobile devices, or associated with smart infrastructure orremote servers), and/or via computer-executable instructions stored onnon-transitory computer-readable media or medium. A processor or aprocessing element may be trained using supervised or unsupervisedmachine learning, and the machine learning program may employ a neuralnetwork, which may be a convolutional neural network, a deep learningneural network, or a combined learning module or program that learns intwo or more fields or areas of interest. Machine learning may involveidentifying and recognizing patterns in existing data in order tofacilitate making predictions for subsequent data. For instance, machinelearning may involve identifying and recognizing patterns in existingtext or voice/speech data in order to facilitate making predictions forsubsequent data. Voice recognition and/or word recognition techniquesmay also be used. Models may be created based upon example inputs inorder to make valid and reliable predictions for novel inputs.Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as drone, autonomous or semi-autonomous drone, image, mobiledevice, vehicle telematics, smart or autonomous vehicle, and/orintelligent home telematics data. The machine learning programs mayutilize deep learning algorithms that may be primarily focused onpattern recognition, and may be trained after processing multipleexamples. The machine learning programs may include Bayesian programlearning (BPL), voice recognition and synthesis, image or objectrecognition, optical character recognition, and/or natural languageprocessing—either individually or in combination. The machine learningprograms may also include natural language processing, semanticanalysis, automatic reasoning, and/or machine learning. In supervisedmachine learning, a processing element may be provided with exampleinputs and their associated outputs, and may seek to discover a generalrule that maps inputs to outputs, so that when subsequent novel inputsare provided the processing element may, based upon the discovered rule,accurately predict the correct output. In unsupervised machine learning,the processing element may be required to find its own structure inunlabeled example inputs.

ML model unit 206 and training unit 220 may be used to train multiple MLmodels relating to different granular segments of vehicle operators. Forexample, training unit 220 may be used to train an ML model foranalyzing injuries in motorcycle operators of a certain age range. Inanother embodiment, training unit 220 may be used to train an ML modelfor use in predicting an appropriate tier assignment/routing foroperators in a particular state or locality. ML model unit 206 may trainone or more artificial neural network, or simply “neural network.” Theneural network may be any suitable type of neural network, including,without limitation, a recurrent neural network or feed-forward neuralnetwork. The neural network may include any number (e.g., thousands) ofnodes or “neurons” arranged in multiple layers, with each neuronprocessing one or more inputs to generate a decision or other output. Insome embodiments, neural network models may be chained together, so thatoutput from one model is fed into another model as input. For example,claim analysis engine 204 may, in one embodiment, apply input data 202to a first neural network model that is trained to generate determinewhether claims include an injury. The output of this first neuralnetwork model may be fed as input to a second neural network model whichhas been trained to predict severity of claims based upon the severityindicia discussed herein. Training unit 220 may train an ANN to identifyinjury claims by accessing electronic claims within historical data 208.Historical data 208 may comprise a corpus of documents comprising many(e.g., millions) of insurance claims which may contain data linking aparticular customer or claimant to one or more vehicles, and which mayalso contain, or be linked to, information pertaining to the customer.The neural network may identify one or more insurance types associatedwith the one or more portions of input data 202 (e.g., bodily injury,property damage, collision coverage, comprehensive coverage, liabilityinsurance, med pay, or personal injury protection (PIP) insurance). Inone embodiment, the one or more insurance types may be identified bytraining the neural network based upon types of peril. For example, theneural network model may be trained to determine a car accidentinvolving damage to a non-covered vehicle may indicate liabilityinsurance coverage.

The claim analysis engine 204 and/or the ML model unit 206 may includeinstructions for receiving and/or retrieving information from thehistorical data 208. The historical data 208 may include one or moreelectronic databases (e.g., SQL or no-SQL databases) and/or flat filedata sources which may contain historical claim data. Historical claimdata may include claim data scanned or entered into a digital format bya human or by an automated process (e.g., via a scanner) from papersources/files and/or electronic claim data. In an embodiment,retrieving/receiving information from the historical data 208 mayinclude performing optical character recognition techniques on textualdata stored in image file formats, and/or performing natural languageprocessing techniques on data stored in audio file formats. In someembodiments, claim analysis engine 204 may determine facts regardingclaim inputs (e.g., the amount of money paid under a claim). Amounts maybe determined in a currency- and inflation-neutral manner, so that claimloss amounts may be directly compared. In some embodiments, claimanalysis unit 204 may search textual claim data for specific strings orkeywords in text (e.g., “hospital”, “ambulance”, etc.) which may beindicative of particular injuries. In some embodiments, text analysismay include text processing algorithms (e.g., lexers and parsers,regular expressions, etc.) and may emit structured text in a formatwhich may be consumed by other components. Standard natural languageprocessing techniques may be used to identify, for example, entities orconcepts that may be indicative of injury (e.g., that an injury occurredto a person, and that the person's leg was injured, that the person wastaken via ambulance to a hospital, etc.).

Claim analysis engine 204 and ML model unit 206 may analyze one or moreclaims. Specifically, the historical data 208 may include a set ofclaims, and a subset of claims may be retrieved from the historical data208 according to any query strategy. For example, claims 210-1 through210-n may be retrieved by claim analysis engine 204, wherein claims210-1 through 210-n correspond to claims in a certain time period,involving a particular vehicle type/classification (e.g., by vehiclemake/model), wherein a particular type of injury was present, etc. Anynumber of claims may be stored in the historical data 208; i.e., n maybe any positive integer. The historical data 208 may include claimslabeled by severity, wherein (for example) a severity level is assignedto each claim on a scale from 0.0-1.0, with 1.0 being the most severeclaim severity level. The historical claim data 208 may then be queriedto retrieve claims having a severity within a particular range. In anembodiment, retrieving/receiving information from the historical data208 may include analyzing image and/or video data to extract information(e.g., to identify damage and/or injury). For example, in an embodiment,instructions executing in claim analysis engine 204 may analyze a claimin claim 210-1 thorough 210-n to determine whether the claim includes animage of a damaged vehicle. A damage score may be assigned to thedamaged vehicle indicating the severity of damage to the vehicle. Eachof the claims 210-1 through 210-n may include an indication of whetheran attorney and/or another party represents a claimant/operator relatingto each respective claim, in addition to information relating to anylegal or quasi-legal proceedings pending and/or settled. In anembodiment, the training process may be performed in parallel, and MLtraining unit 220 may analyze all or a subset of claims 210-1 through210-n. Claim records 110-1 through 110-n may be organized in a flat liststructure, in a hierarchical tree structure, or by means of any othersuitable data structure. For example, the claim records may be arrangedin a tree wherein each branch of the tree is representative of one ormore customer. There, each of claim records 210-1 through 210-n mayrepresent a single non-branching claim, or may represent multiple claimrecords arranged in a group or tree. Further, claim records 210-1through 210-n may comprise links to customers and vehicles whosecorresponding data is located elsewhere. In this way, one or more claimsmay be associated with one or more customers and one or more vehiclesvia one-to-many and/or many-to-one relationships. The status of claimrecords may be completely settled or in various stages of settlement.

As noted, the customer data 212 may include information relating to anoperator/policy holder, such as demographic information (e.g., name,address, telephone number, etc.). The customer data 212 may also includeinformation relating to vehicles owned/operated by the operator/policyholder, as well as detailed information relating to any insurance policyand related coverages. For example, the customer data 212 may include alist of a collision, comprehensive, and/or liability auto insurancepolicy of a claimant. The customer data 212 may include demographic andpolicy information corresponding to claims included in the historicaldata 208 and/or current customers, whose data may be included in theinput data 202. In this way, the claim analysis engine 204 may combineand/or correlate (e.g., by performing an SQL JOIN operation) the inputdata 202, the historical data 208, and or the customer data 212;including any possible subsets thereof.

As used herein, the term “claim” or “vehicle claim” generally refers toan electronic document, record, or file, that represents an insuranceclaim (e.g., an automobile insurance claim) submitted by a policy holderof an insurance company. Herein, “claim data” or “historical data”generally refers to data directly entered by the customer or insurancecompany including, without limitation, free-form text notes,photographs, audio recordings, written records, receipts (e.g., hoteland rental car), and other information including data from legacy,including pre-Internet (e.g., paper file), systems. Notes from claimadjusters and attorneys may also be included. Claim data may includedata entered by third parties, such as information from a repair shop,hospital, doctor, police report, etc.

In one embodiment, claim data may include claim metadata or externaldata, which generally refers to data pertaining to the claim that may bederived from claim data or which otherwise describes, or is related to,the claim but may not be part of the electronic claim record. Claimmetadata may have been generated directly by a developer of theenvironment 200, for example, or may have been automatically generatedas a direct product or byproduct of a process carried out in theenvironment 200. For example, claim metadata may include a fieldindicating whether a claim was settled or not settled, and amount of anypayouts, and the identity of corresponding payees.

Another example of claim metadata is the geographic location in which aclaim is submitted, which may be obtained via a global positioningsystem (GPS) sensor in a device used by the person or entity submittingthe claim. Yet another example of claim metadata includes a category ofthe claim type (e.g., collision, liability, uninsured or underinsuredmotorist, etc.). For example, a single claim in the historical data 208may be associated with a married couple, and may include the name,address, and other demographic information relating to the couple.Additionally, the claim may be associated with multiple vehicles ownedor leased by the couple, and may contain information pertaining to thosevehicles including without limitation, the vehicles' make, model, year,condition, mileage, etc. The claim may include a plurality of claim dataand claim metadata, including metadata indicating a relationship orlinkage to other claims in the historical claim data 208.

Once the ML model has been trained, the claim analysis engine 204 mayapply the trained neural network to the input data 202. In oneembodiment, the input data 202 may merely “pass through” withoutmodification. The output of the ML model, indicating tier and/or injurysegments, and/or severity levels, may be provided to another component,stored in an electronic database, etc. In some embodiments, determininga single label may require a trained ML model to analyze severalattributes within the input data 202. And in some embodiments, multipleinjuries in the input data 202 may be assigned to multiple different (orsame) respective tiers. The respective tiers may correspond to tier108-1 through tier 108-n of FIG. 1. In addition, the ML model mayinclude instructions for classification of one or more respectivedetected injuries into one or more non-overlapping sets of injurysegments/tiers. For example, an injury to a driver may be assigned totier a, b, and c whereas an injury to a passenger may be assigned totiers x, y, and z. Either a strict assignment, to one or more tiers, ora routing, may be output by the ML model. For example, an ML model mayoutput a set {x}, wherein x is a single tier to which a particular claimis assigned. In an embodiment, an ML model may output a set withmultiple members. In yet another embodiment, an ML model may output arouting, wherein a claim will be routed from one tier and/or injurysegment to another, throughout the life of the claim (e.g., (x→y→z),wherein the claim will first be assigned to tier x, then to tier y, thenfinally to tier z. A routing may be created by ordering an output of theML model recommending multiple tiers, from greatest to least confidence,in an embodiment. The routing may also be determined by analyzing theclaim volume and relative backlog of each of a set of tiers, andfactoring both the confidence and backlog into the routing decision.

Exemplary Model Training System

FIG. 3 depicts a high-level block diagram of a claim assignment and/orclaim routing model training/use environment 300 which may facilitatethe training and use of ML models, according to an embodiment. FIG. 3may correspond to one embodiment of the environment 100 of FIG. 1 and/orFIG. 2, and also includes various user/client-side components. Theenvironment 300 may include a client device 302 and a server device 304.Either the client device 302 and/or server device 304 may be anysuitable computing device (e.g., a laptop, smart phone, tablet, server,wearable device, etc.). Server 304 may host services relating to MLmodel training and operation, and may be communicatively coupled toclient device 302 via a network 306.

Although only one client device is depicted in FIG. 3, it should beunderstood that any number of client devices 302 may be supported. Theclient device 302 may include a central processing unit (CPU) 308 and amemory 310 for storing and executing, respectively, a module 312. Whilereferred to in the singular, the CPU 308 may include any suitable numberof processors of one or more types (e.g., one or more CPUs, graphicsprocessing units (GPUs), cores, etc.). Similarly, the memory 310 mayinclude one or more persistent memories (e.g., a hard drive and/or solidstate memory). The module 312, stored in the memory 310 as a set ofcomputer-readable instructions, may be related to an input datacollection application 314 which, when executed by the CPU 308, causesinput data to be stored in the memory 310. The data stored in the memory310 may correspond to, for example, raw data retrieved from the injuryclaim 102, the input data 202, and/or the claim 210-1 through 210-n. Theinput data collection application 314 may be implemented as web page(e.g., HTML, JavaScript, CSS, etc.) and/or as a mobile application foruse on a standard mobile computing platform. The input data collectionapplication 314 may store information in the memory 310, including theinstructions required for the execution of the input data collectionapplication 314 execution. While the user is using the input datacollection application 314, scripts and other instructions comprisingthe input data collection application 314 may be represented in thememory 208 as a web or mobile application. The input data collected bythe input data collection application 314 may transmitted to the serverdevice 304 by a network interface 318 via network 306, where the inputdata may be processed as described above to determine a set of one ormore tier and/or injury segment.

The client device 302 may also include a GPS sensor, an image sensor, auser input device 330 (e.g., a keyboard, mouse, touchpad, and/or otherinput peripheral device), and display interface 332 (e.g., an LEDscreen). The user input device 330 may include components that areintegral to the client device 302, and/or exterior components that arecommunicatively coupled to the client device 302, to enable the clientdevice 302 to accept inputs from the user. The display 332 may be eitherintegral or external to the client device 302, and may employ anysuitable display technology. In some embodiments, the input device 330and the display device 332 are integrated, such as in a touchscreendisplay. Execution of the module 312 may further cause the processor 308to associate device data collected from client 302 such as a time, date,and/or sensor data (e.g., a camera for photographic or video data) withvehicle and/or customer data, such as data retrieved from customer data212. The customer data 212 may be geographically distributed, and may belocated within separate remote servers or any other suitable computingdevices. Distributed database techniques (e.g., sharding and/orpartitioning) may be used to distribute data. In one embodiment, a freeor open source software framework such as Apache Hadoop® may be used todistribute data and run applications. It should also be appreciated thatdifferent security needs, including those mandated by laws andgovernment regulations, may in some cases affect the embodiment chosen,and configuration of services and components.

In some embodiments, the client device 302 may receive data generated byML model unit 206, and may display such data in the display device 332,and/or may use the data to perform a computation (e.g., an applicationexecuting in the module 314 may analyze the data to assign and/or routea claim to a tier and/or injury segment). Execution of the module 312may further cause the CPU 310 communicate with the CPU 340 of the serverdevice 304 via the network interface 318 and the network 306. As anexample, an application related to module 312, such as input datacollection application 314, may, when executed by the CPU 308, cause auser interface to be displayed to a user of client device 302 viadisplay interface 332. The application may include graphical user input(GUI) components for acquiring data (e.g., photographs, text, and/orother components of a loss report) from image sensor 316, the GPSsensor, and textual user input from user input device(s) 330. The CPU308 may transmit the aforementioned acquired data to the server device304, and the CPU 352 may pass the acquired data to an ML model, whichmay accept the acquired data and perform a computation (e.g., trainingof the model, or application of the acquired data to a trained ML modelto obtain a result). With specific reference to FIG. 3, the dataacquired by the client device 302 may be transmitted via the network 306to a server device implementing severity analysis component 104 and/orclaim analysis engine 204, where the data may be processed before beingapplied to a trained ML model by machine learning model unit 206. Theoutput of the trained ML model may be transmitted back to client 302 fordisplay (e.g., in display device 332) and/or for further processing. Thenetwork interface 318 may be configured to facilitate communicationsbetween the client device 302 and the server device 304 via anyhardwired or wireless communication network, including network 306 whichmay be a single communication network, or may include multiplecommunication networks of one or more types (e.g., one or more wiredand/or wireless local area networks (LANs), and/or one or more wiredand/or wireless wide area networks (WANs) such as the Internet). Theclient device 302 and/or the server device 304 may be communicativelycoupled to one or more databases, such as customer data 212 of FIG. 2.

The client device 302 may be, in some embodiments, a client devicecorresponding to one or more of tier set 106. That is, in someembodiments, each tier in tier 108-1 through tier 108-n of FIG. 1 mayinclude one or more client devices 302. The module 312 and/or the module350 may dispatch a claim to one of these client devices 302 belonging totier 108-1 through 108-n in response to an action or event, such as anoutput being generated by the trained ML model. In another embodiment,the output of the trained ML model may identify one or more tier in tierset 106, and the module 350 may assign that one or more tier to theclaim being analyzed, and store an association of the analyzed claim andthe tier set in an electronic database communicatively coupled to theserver device 304.

The server device 304 may include a CPU 340 and a memory 342 forexecuting and storing, respectively, a module 350. The module 350,stored in memory 342 as a set of computer-readable instructions, mayfacilitate applications related to assigning and/or routing of insuranceclaims, including injury data, claim data and claim metadata, andinsurance policy data. The model 350 may also include an ML modeltraining application and/or set of instructions, which may correspond toML training unit 206 and/or training unit 220 of FIG. 2. The model 350may include a set of instructions for receiving/retrieving claims and/orinput data, which may correspond to claim analysis engine 204 of FIG. 2.The model training application 352 may be implemented in any suitabletechnology, including a set of computer instructions for training MLmodels. For example, instructions included in module 350 may cause CPU340 to read data from the historical data 208, which may becommunicatively coupled to the server device 304, either directly or viacommunication network 306. The CPU 340 may include instructions foranalyzing of a series of electronic claim documents from historical data270, as described above with respect to claims 210-1 through 210-n ofhistorical data 208 of FIG. 2. Processor 340 may query customer data 121for data related to respective electronic claim documents and raw data,as described with respect to FIG. 1 and FIG. 2. Module 350 may alsofacilitate communication between client device 302 and server device 304via network interface 352 and network 306. Although only a single serverdevice 304 is depicted in FIG. 3, it should be appreciated that it maybe advantageous in some embodiments to provision multiple server devices304 for the deployment and functioning of the environment 300. Forexample, the claim analysis unit 204 of FIG. 2 may require CPU-intensiveprocessing. Therefore, deploying additional hardware may provideadditional execution speed.

In a manner similar to that discussed above in connection with FIG. 2,historical claims may be ingested by server device 304 and used by modeltraining application 352 to train an ML model. Then, when module 350processes input from client device 302, the data output by the MLmodel(s) (e.g., data indicating severity, tiers, confidence labels,injury segments, injuries, etc.) may be passed to other components, andused for further processing by the other components. It should beappreciated that the client/server configuration depicted and describedwith respect to FIG. 3 is but one possible embodiment. In some cases, aclient device such as client device 302 may not be used. In that case,input data may be entered—programmatically, or manually—directly intoserver device 304. A computer program or human may perform such dataentry. In that case, device may contain additional or fewer components,including input device(s) and/or display device(s).

In operation, a user of the client device 302, by operating the inputdevice 330 and viewing the input display 224, may open input datacollection application 314, which depending on the embodiment, may allowthe user to enter personal information. The user may be an employee of acompany owner/proprietor of the methods and systems disclosed herein, ora customer/end user of the company. For example, input data collectionapplication 314 may walk the user through the steps of submitting a lossreport. Before the user can fully access input data collectionapplication 314, the user may be required to authenticate (e.g., enter avalid username and password). The user may then utilize the input datacollection application 314. The module 312 may contain instructions thatidentify the user and cause the input data collection application 314 topresent a particular set of questions or prompts for input to the user,based upon any information the input data collection application 314collects, including without limitation information about the user or anyvehicle. Further, the module 312 may identify a subset of historicaldata 208 to be used in training a ML model, and/or may indicate toserver device 314 that the use of a particular ML model or models isappropriate. For example, if the user is completing a loss report andselects that a personal injury was sustained, then the module 312 maytransmit the user's name and personal information, the location of theuser as provided by the GPS module, a photograph of the damaged vehiclecaptured by the image sensor 316, a description of the injury, and themake, model, and year of the vehicle to the server device 304.

In some embodiments, location data from the client device 302 may beprovided to the module 350, and the module 350 may select an ML model tooperate based on the location. For example, the zip code of a vehicleoperator, whether provided via GPS or entered manually by a user, maycause the module 350 to select an ML model corresponding to claims in aparticular state, or an ML model corresponding to urban claim filers.

By the time the user of the client device 302 submits a loss reportand/or files a claim, the server device 304 may have already processedthe electronic claim records in historical data 208 and trained one ormore ML model to analyze the information provided by the user to outputtier indications, injury segment indications, and/or weights. Forexample, the operator of a 2018 Chevrolet Camaro may access the clientdevice 302 to submit a loss report under the driver's collisioninsurance policy related to damage to the vehicle sustained when thedriver was rear-ended at a red light. The client 302 may collectinformation from the vehicle operator related to the circumstances ofthe collision, in addition to demographic information of the vehicleoperator, including location and photographs from the GPS module andimage sensor 316, respectively. In some embodiments, the vehicleoperator may be prompted to make and/or receive a telephone call todiscuss the filing of the claim/loss report.

All of the information collected may be associated with a claimidentification number so that it may be referenced as a whole. Theserver device 304 may process the information as it arrives, and thusmay process information collected by input data collection application314 at a different time than the server device 304 processes otherinput. Once information sufficient to process the claim has beencollected, server device 304 may pass all of the processed informationto module 350, which may apply the information to the trained ML model.While the loss report is pending, the client device 302 may display anindication that the processing of the claim is ongoing and/orincomplete. When the claim is ultimately processed by the server 304, anindication of completeness may be transmitted to the client 302 anddisplayed to user, for example via the display device 332. Missinginformation may cause the ML model to abort with an error.

In one embodiment, the settlement of a claim may trigger an immediateupdate of one or more neural network models included in the AI platform.For example, the settlement of a claim involving personal injury thatoccurs in California may trigger updates to a set of personal injury MLmodels pertaining to collision coverage in that State. In addition, oralternatively, as new claims are filed and processed, the weights ofexisting models may be updated, based on online training using the newclaims. In some embodiments, a human reviewer or team of reviewers maybe responsible for approving tier and/or injury segment assignmentsand/or routings before any associated outputs/predictions are used.

While FIG. 3 depicts a particular embodiment, the various components ofenvironment 300 may interoperate in a manner that is different from thatdescribed above, and/or the environment 200 may include additionalcomponents not shown in FIG. 2. For example, an additionalserver/platform may act as an interface between the client device 302and the server device 304, and may perform various operations associatedwith providing the assignment and/or routing information.

Exemplary Claim Processing

The specific manner in which the one or more ML models employ machinelearning to determine the best tier(s) into which to classify injuriesrelating to loss reports may differ depending on the content andarrangement of training documents within the historical data and theinput data provided by customers or users of environment 100,environment 200, and environment 300; as well as the data that is joinedto the historical data and input data, such as customer data 212. Theinitial structure of the ML models may also affect the manner in whichthe trained ML models process the input and claims. The output producedby ML models may be counter-intuitive and very complex. For illustrativepurposes, intuitive and simplified examples will now be discussed inconnection with FIG. 4.

FIG. 4 depicts text-based content of an example electronic claim record400 which may be processed using a trained ML model, such as a modeltrained by model training application 352 of FIG. 3. The term“text-based content” as used herein includes printing (e.g., charactersA-Z and numerals 0-9), in addition to non-printing characters (e.g.,whitespace, line breaks, formatting, and control characters). Text-basedcontent may be in any suitable character encoding, such as ASCII orUTF-8 and text-based content may include HTML. Althoughtext-based-content is depicted in the embodiment of FIG. 4, as discussedabove, claim input data may include images, including hand-writtennotes, and the methods and systems herein may include one or moreindividual model trained to recognize hand-writing and to converthand-writing to text. Further, “text-based content” may be formatted inany acceptable data format, including structured query language (SQL)tables, flat files, hierarchical data formats (e.g., XML, JSON, etc.) oras other suitable electronic objects. In some embodiments, image andaudio data may be fed directly into the neural network(s) without beingconverted to text first.

Electronic claim record 400 may correspond to a claim in claim 210-1through 210-n. With respect to FIG. 4, electronic claim record 400includes three sections 410 a-410 c, which respectively represent policyinformation, loss information, and injury information. Policyinformation 410 a may include information about the insurance policyunder which the claim has been made, including the person to whom thepolicy is issued, the name of the insured and any additional insureds,the location of the insured, etc. Policy information 410 a may bereceived, for example by input data collection application 314, and/orretrieved, for example, from customer data 212. Similarly, vehicleinformation may be included in policy information 410 a, such as avehicle identification number (VIN).

Additional information about the insured and the vehicle (e.g., make,model, and year of manufacture) may be obtained from data sources andjoined to input data. For example, additional customer data may beobtained from customer data 212, and additional vehicle data may beobtained from a vehicle data source. In some embodiments, make and modelinformation may be included in electronic claim record 400, vehicleattributes (e.g., the number of passengers the vehicle seats, theavailable options, etc.) may be queried from another system.

In addition to policy information 410 a, electronic claim record 400 mayinclude loss information 410 b. Loss information generally correspondsto information regarding a loss event in which a vehicle covered by thepolicy listed in policy information 410 a sustained loss, and may be dueto an accident or other peril. Loss information 410 b may indicate thedate and time of the loss, the type of loss (e.g., whether collision,comprehensive, etc.), whether personal injury occurred, what type ofpersonal injury occurred, whether the insured made a statement inconnection with the loss, whether the loss was settled, and if so forhow much money.

In some embodiments, more the than one loss may be represented in lossinformation 410 b. For example, a single accident may give rise tomultiple losses under a given policy, for example to two parties injuredin a crash operated by vehicle operators not covered under the policy.In addition to loss information, electronic claim record 500 may includeinjury information 410 c, which may include indications relating toitems known to be highly correlated to severe injury such as, forexample, whether airbags were deployed, whether a rollover of a vehicleoccurred, and whether there was a spinal injury. It should beappreciated that many additional indicators are envisioned. Externalinformation 510 c may include textual, audio, or video information. Theinformation may include file name references, or may be file handles oraddresses that represent links to other files or data sources, such aslinked data 420 a-g. It should be appreciated that although only links520 a-g are shown, more or fewer links may be included, in someembodiments.

Electronic claim record 500 may include links to other records,including other electronic claim records. For example, electronic claimrecord 500 may link to a loss report 420 a, one or more photograph 420b, one or more investigator's report 420 c, one or more diagram 420 d,one or more legal pleading 420 e, one or more similar 420 f, and one ormore electronic medical record 420 g. Data in links 420 a-420 g may beingested by, for example, the server device 304, the claim analysisengine 204, and/or the severity analysis component 104. For example, asdescribed above, each claim may be ingested and analyzed by the trainedML model.

When a claim is received by server device 304, for example, serverdevice 304 may include instructions that cause, for each link of link420 a-420 g, all available data or a subset thereof to be retrieved.Each link may be processed according to the type of data containedtherein. In some embodiments, a relevance order may be established, andprocessing may be completed according to that order. For example,portions of a claim that are identified as most dispositive of risk maybe identified and processed first. For example, loss report 420 a may beexamined first, to determine whether any personal injury was reported.Alternately, the presence of legal proceedings via pleading 420 e maycause a claim to be assigned to a particular tier. In one embodiment,processing of links 420 a-420 g may automatically abate once aconfidence level with respect to the prediction of an assignment/routereaches a predetermined threshold.

Once the various input data comprising electronic claim record 400 hasbeen processed, the results of the processing may, in one embodiment, bepassed to an ML model. If the ML model that is being trained is a neuralnetwork, then neurons comprising a first input layer of the neuralnetwork may be configured so that each neuron receives particularinput(s) which may correspond, in one embodiment, to one or morerespective fields of policy information 410 a, loss information 410 b,and injury information 410 c. Similarly, one or more input neurons maybe configured to receive particular input(s) from links 420 a-420 g. Insome embodiments, analysis of input entered by a user may be performedon a client device, such as client device 302. In that case, the inputentered by the user may be transmitted to a server, such as serverdevice 304, and may be passed directly as input to an already-trained MLmodel, such as one trained by model training application 352. In anembodiment, a component such as claim analysis engine 204 may save anindication of a tier/injury segment assignment/routing to the electronicclaim file 400, as either part of information 410 a-410 c, or links 420a-420 g.

As noted above, the methods and systems described herein may be capableof analyzing decades of electronic claim records to construct ML models,and the formatting of electronic claim records may change significantlyfrom decade to decade, even year to year. One of the importanttechnological achievements of the methods and systems, which addresses along-felt need in the prior art, is the ability to quickly analyzeelectronic claim records in disparate formats to facilitate consumption,analysis, and comparison. As noted above, the methods and systemsdescribed herein do not suffer from fatigue, cognitive bias, or lack ofexperience.

Exemplary Computer-Implemented Methods

Turning to FIG. 5, an e exemplary computer-implemented method 500 forassigning and/or routing claims to a tier and/or injury segment isdepicted, according to an embodiment. The method 500 may be implementedvia one or more processors, sensors, servers, transceivers, and/or othercomputing or electronic devices. The method 500 may include training anML model to predict the severity of an injury in of electronic vehicleclaim, by analyzing historical electronic vehicle claims (block 502).The method may further include receiving a loss report (block 504). Themethod may further include analyzing the loss report using the trainedML model to predict a loss severity of an injury (block 506). Based onthe loss severity of the injury, the method may generate a set of tiersto which the claim should be assigned and/or routed, and/or the method500 may store an indication of the assignment and/or routing inassociation with the received loss report (block 508). The associationmay be maintained by, for example, a foreign key entry in an electronicdatabase communicatively coupled to server device 304. An assignment maybe a set of tiers that a claim is individually assigned to. On the otherhand, a routing may include a set of tiers in an ordered relationshipsuch as a list, a linked list, balanced binary tree, or any othersuitably ordered data structure. Once the assignment and/or routinginformation is associated with the claim and/or loss report(s), a claimhandler associated with a tier may retrieve a list of claims associatedwith a particular assignment and/or routing.

Although the present invention has been described in considerable detailwith reference to certain preferred versions thereof, other versions arepossible, which may include additional or fewer features. For example,additional knowledge may be obtained using identical methods. Thelabeling techniques described herein may be used in the identificationof fraudulent claim activity. The techniques may be used in conjunctionwith co-insurance to determine the relative risk of pools of customers.External customer features, such as payment histories, may be taken intoaccount in pricing risk. Therefore, the spirit and scope of the appendedclaims should not be limited to the description of the preferredversions described herein.

Benefits of Smart Claim Routing and Smart Claim Assignment

Without the methods and systems described herein, a claim may beassigned and/or routed to a tier/injury segment which is whollyinappropriate to handle the nature and scope of the injury present.Experienced claim analysts/handlers may work in certain tiers, andinexperienced claim analysts/handlers in others. As such, a sever claim(e.g., an accident involving a spinal injury) may be routed to a claimhandler who is new to the job of claim analysis/handling and who is,predictably, unable to handle the claim professionally and to thesatisfaction of the customer. The claim handler may lack the authorityto settle a claim or disburse funds under a claim without review of aclaim handler in another tier. That may cause delays in the processingof claims, which may run afoul of state laws regarding the timelysettlement of claims. On the other hand, a claim involving trivial/minorproperty damage may be assigned/routed to an injury segment tier and maywaste the time of the claim handler. Herein an “injury segment” mayrefer to one or more tiers in a flat list or in a hierarchicalconfiguration. For example an “inpatient” injury segment may refer to aset of injury tiers relating to the handling of claims in which a lossreport includes injuries requiring inpatient hospital treatment.

Reasonable claim handling wait times may be come unreasonable if claimreassignment/rerouting is necessary due to an initial error inrouting/assignment. For example, two claim handling tiers a and b mayeach have a one-week backlog. If a claim c is erroneously assigned totier a and is later re-assigned to tier b, but not until the claim hasstalled during the one-week wait period associated with tier a, then theclaim may not be considered for two weeks, instead of being consideredwithin one week. As the number of re-assignments and/or re-routesincreases, the wait time may constantly scale, resulting in wait timesthat may confuse and/or frustrate customers. The need toreassign/reroute claims that are incorrectly assigned/routed also wastesthe time of claim handlers within the plurality of tiers. Theefficiencies realized by the insurer may cause the overall claimshandling process to be less expensive, which the insurer may pass alongto consumers in the form of lower premiums and other discounts. Themethods and systems described herein may help risk-averse customers tolower their insurance premiums by more granularly quantifying risk. Themethods and systems may also allow new customers to receive moreaccurate pricing when they are shopping for vehicle insurance products.All of the benefits provided by the methods and systems described hereinmay be realized much more quickly than traditional modeling approaches.The methods and systems herein may reduce, in some cases dramatically,insurance company expenses and/or insurance customer premiums, due toincreased efficiencies and improved predictive accuracies. Therefore, itis in the interest of the insurer and customers to utilize the methodsand systems described herein to minimize the wait times for claimprocessing.

Further, the methods and systems disclosed herein may cause additionaltechnical benefits to be realized, which may improve the functioning ofcomputers. For example, by preventing claims to become clogged in theclaims handling process, the necessary amount of hardware storage thatis required to maintain claims may be reduced. For an insurer/proprietorhaving many hundreds of thousands of claims filed each month, computerprocessing resources may also be drastically reduced by preventing alarge claim backlog from being built up over time.

ADDITIONAL CONSIDERATIONS

With the foregoing, any users (e.g., insurance customers) whose data isbeing collected and/or utilized may first opt-in to a rewards, insurancediscount, or other type of program. After the user provides theiraffirmative consent, data may be collected from the user's device (e.g.,mobile device, smart or autonomous vehicle controller, desktop computer,or other device(s)). In return, the user may be entitled insurance costsavings, including insurance discounts for auto, homeowners, mobile,renters, personal articles, and/or other types of insurance. In otherembodiments, deployment and use of neural network models at a userdevice may have the benefit of removing any concerns of privacy oranonymity, by removing the need to send any personal or private data toa remote server.

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement operations or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. These and othervariations, modifications, additions, and improvements fall within thescope of the subject matter herein.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium) or hardware. In hardware, the routines,etc., are tangible units capable of performing certain operations andmay be configured or arranged in a certain manner. In exampleembodiments, one or more computer systems (e.g., a standalone, client orserver computer system) or one or more hardware modules of a computersystem (e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwaremodule that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory product to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory product to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput products, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a building environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a buildingenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for asystem and a process of performing the methods and systems disclosedherein, using the principles disclosed herein. Thus, while particularembodiments and applications have been illustrated and described, it isto be understood that the disclosed embodiments are not limited to theprecise construction and components disclosed herein. Variousmodifications, changes and variations, which will be apparent to thoseskilled in the art, may be made in the arrangement, operation anddetails of the method and apparatus disclosed herein without departingfrom the spirit and scope defined in the appended claims.

What is claimed:
 1. A computer-implemented method of determining aninjury segment based on the severity of the injury, comprising:training, via a processor, a machine learning model using historicalclaim data to determine a severity corresponding to an injury claim,receiving, via a processor, a loss report corresponding to an autoaccident, analyzing the loss report corresponding to the auto accidentusing the trained machine learning model to determine the severity of atleast one injury corresponding to the loss report, determining, based onthe severity of the at least one injury claim corresponding to the lossreport, an injury segment, and storing, via a processor, an indicationof the injury segment in association with the loss report.
 2. Thecomputer-implemented method of claim 1, wherein determining, based onthe severity of the at least one injury claim corresponding to the lossreport, an injury segment includes assigning the loss report to one ormore tiers.
 3. The computer-implemented method of claim 2, wherein theone or more tiers are hierarchically related.
 4. Thecomputer-implemented method of claim 1, wherein determining, based onthe severity of the at least one injury claim corresponding to the lossreport, an injury segment includes assigning the loss report to one ormore ordered tiers, to create a routing.
 5. The computer-implementedmethod of claim 4, wherein the one or more ordered tiers arehierarchically related.
 6. The computer-implemented method of claim 1,wherein the severity is expressed by a numeric severity level.
 7. Thecomputer-implemented method of claim 1, wherein the machine learningmodel is an artificial neural network.
 8. The computer-implementedmethod of claim 1, wherein the loss report corresponding to the autoaccident includes one or both of (i) a photograph corresponding to theaccident, and (ii) a textual description corresponding to the accident.9. The computer-implemented method of claim 1, wherein analyzing theloss report corresponding to the auto accident using the trained machinelearning model to determine the severity of the at least one injurycorresponding to the loss report includes analyzing electronic claimrecords corresponding to the accident.
 10. The computer-implementedmethod of claim 9, further comprising: analyzing vehicle telematicsinformation.
 11. A computer system configured to determine an injurysegment based on the severity of the injury, the system comprising oneor more processors configured to: train, via the one or more processors,a machine learning model using historical claim data to determine aseverity corresponding to an injury claim, receive, via the one or moreprocessors, a loss report corresponding to an auto accident, analyze theloss report corresponding to the auto accident using the trained machinelearning model to determine the severity of at least one injurycorresponding to the loss report, determine, based on the severity ofthe at least one injury claim corresponding to the loss report, aninjury segment, and store, via the one or more processors, an indicationof the injury segment in association with the loss report.
 12. Thecomputer system of claim 11, further configured to: assign the lossreport to one or more tiers.
 13. The computer system of claim 11,further configured to: determine a routing and route the injury claimvia the routing.
 14. The computer system of claim 11, wherein themachine learning model is an artificial neural network.
 15. The computersystem of claim 11, further configured to: analyze vehicle telematicsinformation.
 16. A non-transitory computer readable medium containingprogram instructions that when executed, cause a computer to: train, viathe one or more processors, a machine learning model using historicalclaim data to determine a severity corresponding to an injury claim,receive, via the one or more processors, a loss report corresponding toan auto accident, analyze the loss report corresponding to the autoaccident using the trained machine learning model to determine theseverity of at least one injury corresponding to the loss report,determine, based on the severity of the at least one injury claimcorresponding to the loss report, an injury segment, and store, via theone or more processors, an indication of the injury segment inassociation with the loss report.
 17. The non-transitory computerreadable medium of claim 16, containing further program instructionsthat when executed, cause a computer to: assign the loss report to oneor more tiers.
 18. The non-transitory computer readable medium of claim16, containing further program instructions that when executed, cause acomputer to: determine a routing and route the injury claim via therouting.
 19. The non-transitory computer readable medium of claim 16,containing further program instructions that when executed, cause acomputer to: analyze vehicle telematics information.
 20. Thenon-transitory computer readable medium of claim 16, wherein the machinelearning model is an artificial neural network.